sklearn CountVectorizerscikit-learn’s CountVectorizer to encode text dataCountVectorizer: Transforms text into a matrix of token countsmax_features: Control the number of features used in the modelmax_df, min_df: Control document frequency thresholdsngram_range: Defines the range of n-grams to be extractedstop_words: Enables the removal of common words that are typically uninformative in most applications, such as “and”, “the”, etc.Select all of the following statements which are TRUE.
handle_unknown="ignore" would treat all unknown categories equally.max_features hyperparameter of CountVectorizer the training score is likely to go up.CountVectorizer. If you encounter a word in the validation or the test split that’s not available in the training data, we’ll get an error.cross_validate, each fold might have slightly different number of features (columns) in the fold.X and y is linear.Ridge vs. LinearRegressionRidge adds a parameter to control the complexity of a model. Finds a line that balances fit and prevents overly large coefficients.LinearRegression
Ridge
Ridge.Select all of the following statements which are TRUE.
alpha of Ridge is likely to decrease model complexity.Ridge can be used with datasets that have multiple features.Ridge, we learn one coefficient per training example.Select all of the following statements which are TRUE.
C hyperparameter increases model complexity.